Reformatting multiple paragraphs of text using the formatting of a sample object by creating multiple candidate combinations and selecting a closest match

ABSTRACT

The invention relates to electronic document processing. Embodiments of the present invention relate to a method and apparatus for copying a text format pattern. In one embodiment of the present invention there is a method for copying a text format pattern, including: receiving a selection of a sample object from a user, the sample object including multiple sample paragraphs of which at least two sample paragraphs have different format patterns; receiving a format copying instruction of from the user, the format copying instruction indicating reformatting a target object with the format pattern of the sample object, where the target object contains multiple target paragraphs; determining a corresponding relationship of the format pattern of the sample paragraphs with the target paragraphs; and applying the format pattern of the sample paragraphs to the target paragraphs in accordance with the corresponding relationship.

BACKGROUND

The present invention relates to electronic document processing and,more particularly, to formatting of the content of electronic documents.

An electronic document (hereinafter referred to as “document”) may begenerated by using computer document processing software such as Lotus®Symphony® and Microsoft® Word®, which contains such content as text,charts and graphics, etc.

In the process of generating a document using document processingsoftware, format pattern of the content of the document may beconfigured for the content of the document. For example, font style,font and color, etc., may be configured for textual content.

To facilitate users to set the format pattern for the content of adocument, some document processing software allows users to apply theformat pattern of one content to another, so that the latter will havethe same format pattern as the former.

For example, Microsoft® Word® provides a tool called “Format Painter”. Auser that is in editing a document can apply or copy the format patternof existing texts in the document to other texts. If the user wishes touse a text format pattern already existed in the document, he/she mayselect the text or graphics (or “sample object”) of which the formatpattern will be copied, click the “Format Painter”

icon on the “common” toolbar, and then click the text or graphics (or“target object”) which is to be formatted. As a result, the formatpattern of the target object will become the same as the sample object.

With the above method of copying format pattern, only one format patternmay be copied each time. If the sample object contains multipleparagraphs and each paragraph has a different type of format pattern,then the above method can only apply the format pattern of one paragraphcontained in the sample object, but it can not simultaneously apply theformat patterns of individual paragraphs to different paragraphs of thetarget object.

SUMMARY

The invention is intended to improve the operation of copying a textformat pattern.

In one aspect, a method for copying a text format pattern, includes:receiving a selection of a sample object from a user, the sample objectincluding multiple sample paragraphs of which at least two sampleparagraphs have different format patterns; receiving a format copyinginstruction of from the user, the format copying instruction indicatingreformatting a target object with the format pattern of the sampleobject, where the target object contains multiple target paragraphs;determining a corresponding relationship of the format pattern of thesample paragraphs with the target paragraphs; and applying the formatpattern of the sample paragraphs to the target paragraphs in accordancewith the corresponding relationship.

In another aspect, an apparatus for copying a text format pattern,includes: a sample selection receiving device, configured to receive aselection of a sample object from a user, the sample object includingmultiple sample paragraphs of which at least two sample paragraphs havedifferent format patterns; a copy command receiving device, configuredto receive a format copying instruction of from the user, the formatcopying instruction indicating reformatting a target object with theformat pattern of the sample object, where the target object containsmultiple target paragraphs; a parsing device, configured to determine acorresponding relationship of the format pattern of the sampleparagraphs with the target paragraphs; and a format copying device,configured to apply the format pattern of the sample paragraphs to thetarget paragraphs based on the corresponding relationship determined bythe parsing device.

With the inventive method or apparatus, multiple format patterns of adocument fragment can be copied to another document fragmentsimultaneously each time, thus simplifying the operation of the user inediting electronic document.

BRIEF DESCRIPTION OF THE DRAWINGS

Through reading the following detailed description of illustrativeembodiments of the disclosure with reference to drawings, the above andother objectives, features and advantages of the disclosure will becomeclearer. In the illustrative embodiments, the same reference numberusually represents the same component.

FIG. 1 shows a block diagram of an exemplary computing system 100adapted to be used to implement embodiments of the invention;

FIG. 2A illustrates a sample object of an electronic document;

FIG. 2B illustrates a target object of an electronic document;

FIG. 2C illustrates the result of formatting the target object shown inFIG. 2B according to an embodiment of the invention;

FIG. 2D illustrates the result of formatting the target object shown inFIG. 2B in accordance with a prior art technique;

FIG. 3 illustrates a flowchart of the method of copying a text formatpattern according to an embodiment of the invention;

FIG. 4 illustrates a flowchart of a first method of determining acorresponding relationship between sample paragraphs and targetparagraphs;

FIG. 5A shows a sample tree representing a sample object according to anembodiment of the invention;

FIG. 5B shows a candidate tree representing a target object according toan embodiment of the invention;

FIG. 5C shows a matching tree according to an embodiment of theinvention;

FIG. 5D is a simplified representation of a plurality of candidate treesaccording to an embodiment of the invention; and

FIG. 6 illustrates a block diagram of the apparatus for copying a textformat pattern according to an embodiment of the invention.

DETAILED DESCRIPTION

Preferred embodiments of the present disclosure will be described ingreater detail below with reference to the accompanying drawings. Theaccompanying drawings have shown those preferred embodiments of thepresent disclosure, however, it should be understood that, the presentdisclosure can be implemented in various forms, but are not limited tothese embodiments illustrated herein. On the contrary, these embodimentsare provided for making the present disclosure more thorough andcomplete, such that the scope of the present disclosure can becompletely delivered to one of ordinary skill in the art.

FIG. 1 shows a block diagram of an exemplary computing system 100 whichis applicable to implement the embodiments of the present invention. Asshown in FIG. 1, the computing system 100 may include: CPU (CentralProcessing Unit) 101, RAM (Random Access Memory) 102, ROM (Read OnlyMemory) 103, System Bus 104, Hard Drive Controller 105, KeyboardController 106, Serial Interface Controller 107, Parallel InterfaceController 108, Display Controller 109, Hard Drive 110, Keyboard 111,Serial Peripheral Equipment 112, Parallel Peripheral Equipment 113 andDisplay 114. Among above devices, CPU 101, RAM 102, ROM 103, Hard DriveController 105, Keyboard Controller 106, Serial Interface Controller107, Parallel Interface Controller 108 and Display Controller 109 arecoupled to the System Bus 104. Hard Drive 110 is coupled to Hard DriveController 105. Keyboard 111 is coupled to Keyboard Controller 106.Serial Peripheral Equipment 112 is coupled to Serial InterfaceController 107. Parallel Peripheral Equipment 113 is coupled to ParallelInterface Controller 108. And, Display 114 is coupled to DisplayController 109. It should be understood that the structure as shown inFIG. 1 is only for the exemplary purpose rather than any limitation tothe present invention. In some cases, some devices may be added to orremoved from the computer system 100 based on specific situations.

As will be appreciated by one of ordinary skill in the art, aspects ofthe present invention may be embodied as a system, method or computerprogram product. Accordingly, aspects of the present invention may takethe form of an entirely hardware embodiment, an entirely softwareembodiment (including firmware, resident software, micro-code, etc.) oran embodiment combining software and hardware aspects that may allgenerally be referred to herein as a “circuit,” “module” or “system.”Furthermore, aspects of the present invention may take the form of acomputer program product embodied in one or more computer readablemedium(s) having computer readable program code embodied thereon.

Any combination of one or more computer readable medium(s) may beutilized. The computer readable medium may be a computer readable signalmedium or a computer readable storage medium. A computer readablestorage medium may be, for example, but not limited to, an electronic,magnetic, optical, electromagnetic, infrared, or semiconductor system,apparatus, or device, or any suitable combination of the foregoing. Morespecific examples (a non-exhaustive list) of the computer readablestorage medium would include the following: an electrical connectionhaving one or more wires, a portable computer diskette, a hard disk, arandom access memory (RAM), a read-only memory (ROM), an erasableprogrammable read-only memory (EPROM or Flash memory), an optical fiber,a portable compact disc read-only memory (CD-ROM), an optical storagedevice, a magnetic storage device, or any suitable combination of theforegoing. In the context of this document, a computer readable storagemedium may be any tangible medium that can contain, or store a programfor use by or in connection with an instruction execution system,apparatus, or device.

A computer readable signal medium may include a propagated data signalwith computer readable program code embodied therein, for example, inbaseband or as part of a carrier wave. Such a propagated signal may takeany of a variety of forms, including, but not limited to,electro-magnetic, optical, or any suitable combination thereof. Acomputer readable signal medium may be any computer readable medium thatis not a computer readable storage medium and that can communicate,propagate, or transport a program for use by or in connection with aninstruction execution system, apparatus, or device.

Program code embodied on a computer readable medium may be transmittedusing any appropriate medium, including but not limited to wireless,wire, optical fiber cable, RF, etc., or any suitable combination of theforegoing.

Computer program code for carrying out operations for aspects of thepresent invention may be written in any combination of one or moreprogramming languages, including an object oriented programming languagesuch as Java, Smalltalk, C++ or the like and conventional proceduralprogramming languages, such as the “C” programming language or similarprogramming languages. The program code may execute entirely on theuser's computer, partly on the user's computer, as a stand-alonesoftware package, partly on the user's computer and partly on a remotecomputer or entirely on the remote computer or server. In the latterscenario, the remote computer may be connected to the user's computerthrough any type of network, including a local area network (LAN) or awide area network (WAN), or the connection may be made to an externalcomputer (for example, through the Internet using an Internet ServiceProvider).

Aspects of the present invention are described below with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems) and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer program instructions. These computer program instructions maybe provided to a processor of a general purpose computer, specialpurpose computer, or other programmable data processing apparatus toproduce a machine, such that the instructions, which execute via theprocessor of the computer or other programmable data processingapparatus, create means for implementing the functions/acts specified inthe flowchart and/or block diagram block or blocks.

These computer program instructions may also be stored in a computerreadable medium that can direct a computer, other programmable dataprocessing apparatus, or other devices to function in a particularmanner, such that the instructions stored in the computer readablemedium produce an article of manufacture including instructions whichimplement the function/act specified in the flowchart and/or blockdiagram block or blocks.

The computer program instructions may also be loaded onto a computer,other programmable data processing apparatus, or other devices to causea series of operational steps to be performed on the computer, otherprogrammable apparatus or other devices to produce a computerimplemented process such that the instructions which execute on thecomputer or other programmable apparatus provide processes forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks.

The flowcharts and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof code, which includes one or more executable instructions forimplementing the specified logical function(s). It should also be notedthat, in some alternative implementations, the functions noted in theblock may occur out of the order noted in the figures. For example, twoblocks shown in succession may, in fact, be executed substantiallyconcurrently, or the blocks may sometimes be executed in the reverseorder, depending upon the functionality involved. It will also be notedthat each block of the block diagrams and/or flowchart illustration, andcombinations of blocks in the block diagrams and/or flowchartillustration, can be implemented by special purpose hardware-basedsystems that perform the specified functions or acts, or combinations ofspecial purpose hardware and computer instructions.

The present invention relates to processing electronic documents withcomputer document processing software that may be executed on a computersystem 100 as shown in FIG. 1.

To facilitate the understanding of the invention, reference is madefirst to FIG. 2A. FIG. 2A illustrates a document fragment 210 of anelectronic document. The document fragment 210 contains a plurality ofparagraphs 211, 212, . . . , 216, wherein at least two paragraphs havedifferent format patterns. For example, the paragraph 211 contains thetext “Active Event” with the font of “Times”, the font style of “plain”and the color of “green”. In addition, there are two adjacent paragraphs214 and 215 that have the same format pattern.

In the present specification, a document fragment like the documentfragment 210 is referred to as “sample object”, and a paragraph like theparagraph 211, 212, . . . 216 contained in the document fragment isreferred to as “sample paragraph”.

FIG. 2B illustrates a document fragment 220 of another electronicdocument. The document fragment 220 as shown contains multipleparagraphs P1, P2, . . . P7, each paragraph having a certain formatpattern. For example, the paragraph P1 contains the text “Active Event”with the font of “Times”, the font style of “plain” and the color of“black.” In addition, the paragraphs P4, P5 and P6 have the same formatpattern, for example, their font styles are all “italic”.

Suppose the user who is editing the document fragment 220 wants to applythe format pattern of the sample object 210 to the document fragment220. In the present specification, the document fragment like thedocument fragment 220 is referred to as the “target object”, and theparagraphs P1, P2, . . . P7 contained in the target object 220 arereferred to as the “target paragraphs”.

The sample object 210 and the target object 220 may be either twodocument fragments of the same one document, or two document fragmentsfrom different documents. As one situation, a particular user A hasedited a document fragment 220 of a document and configured formatpattern for each paragraphs in the document fragment 220. Later, anotheruser B sends another document to the user A, requiring the user A tore-configure format pattern for the document fragment 220 in accordancewith the format pattern of the document fragment 210 in the anotherdocument.

FIG. 2C shows the result after the format pattern of the sample object210 is copied or applied to the target object 220 according to theuser's expectation. As shown in FIG. 2C, the result is also a documentfragment 230. The document fragment 230 includes paragraphs 231, 232, .. . 237 of which the contents are same as in the target object 220,whereas the format pattern of the paragraphs 231, 232, 233 and 237 issame as the format pattern of the paragraphs 211, 212, 213 and 216 (inFIG. 2A) respectively, and the format pattern of the paragraphs 234, 235and 236 is same as the format pattern of the paragraphs 214 and 215. Inthe present invention, a document fragment like the document fragment230 is referred to as “expected object” and the paragraphs 231, 232, . .. 237 contained in the expected object 230 are referred to as “expectedparagraphs.

As mentioned above, MS Word provides a “Format Painter” tool for a userwho is editing a document to apply (or copy) the format pattern of asample object of the document to a target object. If the user wishes toapply the format pattern of the sample object 210 to the target object220, the following process needs to be performed:

-   -   Select the paragraph 211;    -   Click on the “Format Painter”        in the toolbar; and    -   Select the paragraph P1.

As a result, the format pattern of the paragraph P1 becomes the same asthe paragraph 211.

Repeat the above process for paragraphs P2, P3, P4-P6 and P7. As aresult, the format pattern of the target object 220 will become the sameas the format pattern of the sample object 210.

If the user takes the entire target object 220 as a unit and attempt toapply the format pattern of the sample object 210, then it is equivalentto performing the following operations:

Select the sample object 210;

Click on the “Format Painter”

in the toolbar; and

Select the target object 220.

The result of the above operations is shown in FIG. 2D. In FIG. 2D, thecontent of the document object 240 is the same as the target object 220,but the format pattern of each paragraphs of the target object 220becomes the same as the format pattern of the paragraph 211. This maynot be the desired result of the user.

The basic idea of the invention is that, by analyzing the sample object210 and the target object 220, it is made possible to copy the formatpattern of the sample object 210 with the target object 220 containingmultiple paragraphs as a unit.

Hereinafter, description of various embodiments of the invention will beprovided with reference to the accompanying drawings.

Referring now to FIG. 3, which illustrates a flowchart of the method forcopying a text format pattern in accordance with an embodiment of theinvention. The method illustrated may be implemented in an electronicdocument processing application running on the computer system 100.

In Step 310, the selection of a sample object is received from a user,the sample object including multiple sample paragraphs of which at leasttwo sample paragraphs have different format patterns.

For example, a user in editing an electronic document may select asample object in the electronic document by an input tool such as amouse. The sample object includes a plurality of sample paragraphs,wherein at least two sample paragraphs have different format patterns.An example of the sample object mentioned in Step 310 is the sampleobject 210 shown in FIG. 2A, which includes six sample paragraphs211-216 with at least two sample paragraphs (e.g., 211 and 212) havingdifferent format patterns.

Those skilled in the art shall appreciate that, in practicalapplications, Step 310 may be implemented in various ways, for example,in a manner similar to the way of receiving a user's selection of asample object in the MS Word application. Further explanation in detailsis not needed here.

It is to be noted that, the content of the sample paragraph may beeither textual or of other forms that can be formatted, for example, atable. Sample paragraphs may be separated either by a “carriage return”symbol or other pre-defined delimiters.

In Step 320, a format copying instruction is received from the user, theformat copying instruction indicating re-formatting a target object withthe format pattern of the sample object, where the target objectincludes multiple target paragraphs.

Those skilled in the art shall appreciate that, in practicalapplications, Step 320 may be implemented in various ways. For example,it may be implemented in a similar manner to using the MS Word “FormatPainter”. In that case, a “copy format” option may be provided on thetoolbar of the user interface of the electronic document processingapplication. The user may select a sample object 210 first, then clickthe “copy format” option on the toolbar, and then select a targetobject, for example, the target object 220. This indicates that the userhas sent out an instruction requesting for re-formatting the targetobject 220 in accordance with the selected sample object 210.

The target object to be formatted contains several paragraphs, namelytarget paragraphs. For example, the target object 220 contains seventarget paragraphs P1-P7.

Similar to the sample paragraph, the content of the target paragraph maybe either textual or of other forms that can be formatted, for example,a table. Object paragraphs may be separated by either a “carriagereturn” symbol or other pre-defined delimiters.

The number of target paragraphs is usually equal to or greater than thenumber of format patterns contained in the sample paragraph.

In Step 330, the corresponding relationship between the format patternof the sample paragraphs and the target paragraphs is determined.

The sample object and the target object are selected by the user for thepurpose of copying format pattern. Therefore, there is intrinsiccorresponding relationship between the format pattern of sampleparagraphs and the format pattern of the object paragraphs. By analyzingand comparing the characteristics of the sample paragraphs and thecharacteristics of the target paragraphs, such as different formatpatterns of the sample paragraphs contained in the sample object and thenumber of and format patterns of the target paragraphs contained in thetarget object, the corresponding relationship between the format patternof the sample paragraphs and the target paragraphs may be identified.

In accordance with an embodiment of the invention, the correspondingrelationship of the format pattern of the sample paragraphs with thetarget paragraphs may be determined by comparing the positionalrelationship between the sample paragraphs and the target paragraphs.

An intuitive simple situation includes that the number of sampleparagraphs is equal to the number of target paragraphs, for example, asample object contains n sequential sample paragraphs s1, s2, . . . sn,and a target object also contains n sequential target paragraph t1, t2,. . . tn. Through analysis and comparison, it may be determined thatthere is a corresponding relationship between the sample paragraph atthe i-th (i=1 . . . n) position and the target paragraph at the i-thposition, i.e.:s1<->t1; s2<->t2, . . . sn<->tn

Thus, it may be determined that there is a corresponding relationshipbetween the format pattern of the i-th positioned sample paragraph andthe i-th positioned target paragraph.

In accordance with an embodiment of the invention, by comparing therelationship between the format pattern of sample paragraphs and theposition of target paragraphs, the corresponding relationship of theformat pattern of the sample paragraphs with the target paragraphs maybe determined.

For example, the number of format patterns of sample paragraphs is equalto the number of sample paragraphs, e.g., the sample paragraphs of asample object have sequentially n format patterns f1, f2, . . . fn, atarget object contains n sequential target paragraphs t1, t2, and . . .tn. Through analysis and comparison, it may be determined that there iscorresponding relationship between the i-th format pattern of the sampleparagraphs of the sample object and the i-th positioned targetparagraphs, namely:f1<->t1; f2<->t2, . . . fn<->tn

Below, in conjunction with the accompanying FIGS. 5A-5D, embodiments ofdetermining the corresponding relationship of the format pattern of thesample paragraphs with the target paragraphs by comparing the positionalrelationship between the sample paragraphs and the target paragraphswill be described in more detail.

In accordance with an embodiment of the invention, the correspondingrelationship of the format pattern of the sample paragraphs with thetarget paragraphs may be determined by analyzing the format pattern ofsample paragraphs and the format pattern of target paragraphs.

The sample object 210 of FIG. 2A and the target object 220 of FIG. 2Bmay be taken as example in order to understand this embodiment. As shownin FIG. 2A, the format pattern of sample paragraphs 211-216 of thesample object 210 sequentially undergoes four changes:

-   -   1. from the format pattern of paragraph 211 to that of paragraph        212;    -   2. from the format pattern of paragraph 212 to that of paragraph        213;    -   3. from the format pattern of paragraph 213 to that of        paragraphs 214 and 215;    -   4. from the format of paragraphs 214 and 215 to that of        paragraph 216.

As shown in FIG. 2B, the format pattern of sample paragraphs P1-P7 ofthe sample object 220 sequentially undergoes four changes:

-   -   1. from the format pattern of paragraph P1 to that of paragraph        P2;    -   2. from the format pattern of paragraph P2 to that of paragraphs        P3;    -   3. from the format pattern of paragraph P3 to that of paragraphs        P4, P5 and P6;    -   4. from the format pattern of paragraphs P4, P5 and P6 to that        of paragraphs P7.

By comparing format pattern changes in the sample paragraphs and thetarget paragraphs, it may be determined that the sample paragraphs andthe target paragraphs have the following corresponding relationship:(211)<->(P1); (212)<->(P2); (213)<->(P3);(214,215)<->(P4, P5, P6); (216)<->(P7)

Thereby, the corresponding relationship of the format pattern of thesample paragraphs with the target paragraphs may be determined. Forexample, there is a corresponding relationship between the formatpattern of sample paragraph 211 and target paragraph P1. In addition,there is a corresponding relationship between the format pattern ofsample paragraphs 214 and 215 and target paragraphs P4, P5 and P6.

According to an embodiment of the invention, the correspondingrelationship of the format pattern of the sample paragraphs with thetarget paragraphs may be determined according to the relationshipbetween the content of the sample paragraphs and the content of thetarget paragraphs.

By conducting semantic analysis on the content of sample paragraphs andthe content of target paragraphs and making a comparison of the contentof the sample paragraphs with the content of the target paragraphs, thecorresponding relationship of the format pattern of the sampleparagraphs with the target paragraphs may be determined based on theresult of the comparison.

For example, the content of the sample of paragraph 211 is “ActiveEvent”, and the content of target paragraph P1 is “Active Event”. Theyare the same. Thus, the corresponding relationship of the format patternof the sample paragraph 211 with the target paragraph P1 may bedetermined. Similarly, since the sample paragraph 216 and the targetparagraph P7 are both of the content “Listen to the audio webcast”, thecorresponding relationship of the format pattern of the sample paragraph216 with the target paragraph P7 may be determined. In some cases,although the content of a sample paragraph and the content of a targetparagraph are not identical, they are similar. Based on that, thecorresponding relationship of the format pattern of the sample paragraphwith the target paragraph may also be determined accordingly. Forexample, suppose the content of the sample paragraph 212 is “Topic:Morgan Stanley Cloud Computing Symposium” instead of “Morgan StanleyCloud Computing Symposium” and the content of the target paragraph P2 is“Topic: Kaufman Brothers Cloud Computing Conference” instead of “KaufmanBrothers Cloud Computing Conference”. Through analysis and comparison ofthe content of the sample paragraph 212 and the content of the targetparagraph P2, it is possible to determine the corresponding relationshipof the format pattern of the sample paragraph 212 with the targetparagraph P2 according to the keyword “Topic”.

After Step 330, Step 340 is performed, in which the format pattern ofthe sample paragraph is applied (or copied) to the corresponding targetparagraph.

After determining the corresponding relationship of sample paragraphformat patterns with target paragraph, the format pattern of a sampleparagraph may be applied to the corresponding target paragraph.

For example, the format pattern of the sample paragraph 211 is <font:Times, font style: plain, color: green>, which is denoted as F211. Suchformatting parameters may be extracted from the sample paragraph 211after it is determined in Step 320 that the user wants to make formatpattern replication.

If it is determined that there is corresponding relationship between theformat pattern of the sample paragraph 211 and the target paragraph P1,then the format pattern F211 of the sample paragraph 211 is applied tothe target paragraph P1. In other words, the target paragraph P1 is madeto have the format pattern of <font: Times, font style: plain, color:green> and become the expected paragraph 231 as shown in FIG. 2C.

Those skill in the art shall appreciate that known techniques in theprior art may be employed for extracting formatting parameters of asample paragraph as well as applying the format pattern of the sampleparagraph to a target paragraph. Further explanation in details is notneeded here.

In the above description regarding Step 330 it is indicated that thecorresponding relationship of the format pattern of the sampleparagraphs with the target paragraphs may be determined by comparing theformat pattern of the sample paragraphs and the position of targetparagraphs. Also, the corresponding relationship of the format patternof the sample paragraphs with the target paragraphs may be determined byanalyzing the format pattern of sample paragraphs and the format patternof target paragraphs. In practical applications, the two ways may becombined. Below, with reference to the accompanying FIGS. 4 and 5A-5D,two exemplary algorithms that implement Step 330 will be furtherillustrated. To facilitate understanding, in the description of thealgorithms the content as shown in FIGS. 2A-2C will be taken as theexample of sample object, target object and expected object.

The first algorithm, depicted in FIG. 4, includes Steps S321, S322, S323and S324.

In Step S321, N sample paragraphs are divided into M (M>1) samplegroups, F1-FM, according to their order and format pattern, so that eachsample group will include adjacent sample paragraphs of same formatpattern.

For example, the sample paragraphs 211-216 shown in FIG. 2A may bedivided into five sample groups, as shown in Table 1.

TABLE 1 Group of Sample Paragraph Sample Group F₁ F₂ F₃ F₄ F₅ SampleParagraphs 211 212 213 214, 215 216

For example, the sample paragraph 211 belongs to sample group F1, theadjacent sample paragraphs 214 and 215 with same format pattern belongto sample group F4. The mapping relationship of the paragraphs and thesample groups shown in Table 1 may be represented by an equivalent treestructure.

FIG. 5A shows a sample tree ST (i.e. sample combination) representingthe mapping relationship of the sample paragraphs and the sample groupsshown in Table 1. The sample tree ST includes three layers of nodes. Theroot node R is a virtual node, the five intermediate nodes F1-F5represent the five sample groups and the six leaf nodes respectivelycorrespond to the sample paragraphs 211-216. The parent-childrelationship between an intermediate node and a leaf node denotes thatthe corresponding sample group includes the corresponding sampleparagraph, and the sample groups F₁-F₅ represents the format pattern ofthe sample paragraphs included. Therefore, in the specification, such anintermediate node is also referred to as “format node”.

In Step S322, different combinations, or “candidate combinations”, aregenerated such that, for each combination, N target paragraphs areassigned to M sample groups.

Below in Table 2, it is shown various combinations of assigning theseven target paragraphs P1-P7 in FIG. 2B to the five sample groupsF₁-F₅.

TABLE 2 Combinations of assigning target paragraphs to sample groups F₁F₂ F₃ F₄ F₅ TED 1 P1 P2 P3 P4 P5, P6, P7 3 2 P1 P2 P3 P4, P5 P6, P7 1 3PI P2 P3 P4, P5, P6 P7 1 4 P1 P2 P3, P4 P5 P6, P7 3 5 P1 P2 P3, P4 P5,P6 P7 1 6 P1 P2 P3 P4, P5 P6, P7 3 7 P1 P2, P3 P4 P5 P6, P7 3 8 P1 P2,P3 P4 P5, P6 P7 1 9 P1 P2, P3 P4, P5 P6 P7 3 10 P1 P2, P3, P4 P5 P6 P7 311 P1, P2 P3 P4 P5 P6, P7 3 12 P1, P2 P3 P4 P5, P6 P7 1 13 P1, P2 P3 P4,P5 P6 P7 3 14 P1, P2 P3, P4 P5 P6 P7 3 15 P1, P1, P3 P4 P5 P6 P7 3

In Table 2, columns 2-6 in the first row represent sample groups F₁-F₅,rows 2-16 represent all candidate combinations. For example, the secondrow represents that, according to the candidate combination 1, P1, P2,P3, P4, and {P5, P6, and P7} are respectively assigned to sample groupsF₁-F₅.

Generally, the number of candidate combinations for assigning N targetparagraphs to M sample groups is n=(N−1)/[(M−1)! (NM)!]. In thisexample, M=5, N=7. Therefore, there are (7-1)!/[(5-1)!(7-5)!]=15 kindsof candidate combinations in Table 2.

A candidate combination may also be represented with an equivalent treestructure.

FIG. 5B shows an equivalent candidate tree CT1 that represents thecandidate combination 1 shown in Table 2. The candidate tree CT1contains three layers of nodes. The root node R and the intermediatenodes F1-F5 of are the same as the sample tree ST as shown in FIG. 5A,the seven leaf nodes respectively correspond to the target paragraphsP1-P7. The parent-child relationship between an intermediate node and aleaf node represent the corresponding relationship between a samplegroup and a target paragraph, i.e. the corresponding relationshipbetween the format pattern of the sample paragraph and the targetparagraph.

All of the candidate combinations 1-15 shown in Table 2 may berepresented as such a candidate tree.

FIG. 5D is a concise representation of equivalent candidates for the 15candidate combinations 1-15 as shown in Table 2. In FIG. 5D, thevertical line “|” is used as a separator that divides the nodes P1-P7into child nodes of the intermediate nodes F₁-F₅. For example, the nodesP1-P7 shown in the second row are separated into 5 groups by thevertical line “|”. The nodes of the five groups are respectively childnodes of the format nodes F1, F2, F3, F4 and F5. Therefore, the secondrow is a concise representation of the candidate tree CT₁ shown in FIG.5B. Similarly, rows 3 to 16 respectively represent candidate treescorresponding to candidate combinations 2-15, denoted as CT₂, CT₃, . . ., CT₁₅.

Generally speaking, a set of such candidate trees, S, includes ncandidate trees, denoted as S={CT1, CT2, . . . , CTn}, wherein acandidate tree CT contains a root node, M intermediate nodes and N leafnodes, and each of the leaf nodes represents a target paragraph,n=(N−1)!/[(M−1)! (N−M)!].\

In Step S323, preferred tree(s) is selected from the set of candidatetrees S, wherein a preferred tree is a candidate tree from the set ofcandidate trees S that has the smallest tree edit distance (TED) withthe sample tree ST.

The tree edit distance (TED) between a candidate tree CT and the sampletree ST is calculated by the following formula:TED=Σ_(i=1) ^(M) |N _(i)(s)−N _(i)(t)|  Formula (1)wherein N_(i)(s) is the number of child nodes of the i-th intermediatenode F_(i) of the sample tree ST, and N_(i)(t) is the number of childnodes of the i-th intermediate node F_(i) of the candidate tree CT.

In fact, the tree edit distance (TED) between a candidate tree CT andthe sample tree ST (simply referred to as “TED for the candidate tree”)is defined to be the sum of the number of operations of deleting a leafnode from the candidate tree and the number of operations of adding aleaf node into the candidate tree as required in order for the candidatetree CT to be converted to the sample tree ST. The TED characterizes thedegree of similarity between the candidate tree CT and the sample treeST. The smaller the TED is, the higher the degree of similarity betweenthe candidate tree CT and the sample tree ST is.

According to Formula (1), the tree edit distance TED between each of thecandidate trees in FIG. 4D and the sample tree ST shown in FIG. 5A maybe calculated, as shown in the column 7 of Table 2.

The column 7 of Table 2 shows the TED of each of the candidate treesCT₁-CT₅ corresponding to fifteen candidate combinations 1-15. Forexample, the TED for the candidate tree CT₁ is 3, denoted as TED(CT₁)=3.

From Table 2 it can be seen thatTED(CT₂)=TED(CT₃)=TED(CT₅)=TED(CT₈)=TED(CT₁₂)=1i.e., the TED of candidate trees CT₂, CT₃, CT₅, CT₈ and CT₁₂ is thesmallest. Therefore, they are selected to be preferred trees.

Hereinafter, a set of candidate trees is denoted by S′.

In Step S324, a matching tree MT is selected from the preferred tree(s).

If there is only one preferred tree in the set S′, this preferred treewill be taken as the matching tree MT. The corresponding relationship ofsample paragraph format patterns with target paragraphs may bedetermined from the relationship among the leaf nodes P1-P7 and theformat nodes F1-F5 of the matching tree MT.

By Steps S321-S324, the relationship among sample paragraph formatpatterns and target paragraph positions may be compared and thecorresponding relationship among sample paragraph format patterns andtarget paragraphs may be determined.

If there are multiple preferred trees in the set S′, then a matchingtree may be determined from the multiple preferred trees by means ofanalyzing sample paragraph format patterns and target paragraph formatpatterns, thereby determining the corresponding relationship of sampleparagraph format patterns with target paragraphs. This may be realizedby a second algorithm.

Generally speaking, for two or more adjacent target paragraphs that willhave the same format pattern after the format-copying process, theirformat patterns are relatively similar prior to the format-copyingprocess. For example, the degree of similarity among format patterns ofthe target paragraphs P4, P5 and P6 is greater than the degree ofsimilarity between format patterns of the target paragraphs P3 and P4.Based on the above observation, the second algorithm determines whichtarget paragraphs will most likely have the same format pattern afterformat-copying based on the original formatting information of thetarget paragraphs, thereby selecting the matching tree.

The second algorithm includes Steps S341-S343.

In Step S341, format patterns of target paragraphs are ascertained.

A format vector V=(X1, X2, . . . Xk) may be used to represent the formatpattern of a target paragraph, wherein each component of the formatvector represents a format attribute.

For example, a format vector v=(x1, x2, . . . x4) may be pre-defined, inwhich the component x1 represents format attribute “plain”, x2represents attribute “italic”, x3 represents the attribute “bold” and x4represents attribute “underlined”. Then the formatting information ofthe target paragraphs P1-P7 in FIG. 2B is shown Table 3.

TABLE 3 Format Vector of Object Paragraph Object Paragraph P1 P2 P3 P4P5 P6 P7 v (1, 0, 0, 0) (0, 0, 1, 0) (1, 0, 0, 0) (0, 1, 0, 0) (0, 1, 0,0) (0, 1, 0, 0) (1, 0, 0, 0)

In Table 3, the first row represents seven paragraphs P1-P7, and eachcolumn on the second row represents the value (x1, x2, x3, x4) of theformat vector of a corresponding target paragraph, wherein component xihas one-to-one correspondence with attribute Xi of the format vectorV=(X1, X2, . . . X4). If the value of a component xi is “1”, it meansthe corresponding attribute Xi is included in the format pattern. If thevalue of a component xi is “0”, it means the corresponding attribute Xiis not included in the format pattern. For example, in Table 3, the cellat row 2 and column 2 represents the format attribute value of theparagraph P1, denoted as v(P1)=(1,0,0,0), which means that the textformat of the paragraph P1 includes “plain”, but does not include“italic”, does not include “bold”, and does not include “underlined”.

In Step S342, for each of preferred trees in the set S′, a correspondingsum of square error (SSE) is calculated according to the followingFormula (2):SSE=ρ_(i=1) ^(M)Σ_(x∈G) _(i) D(c _(i) ,x)  Formula (2)

The meaning of parameters in Formula (2) is illustrated below by way ofexample.

M is the number of intermediate nodes of a preferred tree PT. In thepresent example, M=7.

G_(i) represents a cluster of format vectors of sample paragraphs. Foran intermediate node F_(i) (i=1 . . . M) of any preferred tree PT, thecorresponded cluster G_(i) includes format vectors of sample paragraphsrepresented by all child nodes of the intermediate node F_(i). Forexample, in the present example, CT₂ is one preferred tree from thepreferred tree set S′={CT₂, CT₃, CT₅, CT₈, CT₁₂}. Five clusters thatrespectively correspond to the intermediate node F₁-F₅ of the preferredtree are:G ₁={(1,0,0,0)}; G ₂={(0,0,1,0)}; G ₃={(1,0,0,0)}; G ₄={(0,1,0,0),(0,1,0,0)}; G ₅={(0,1,0,0), (1,0,0,0)}.

c_(i) is the center vector of cluster G_(i), which is the vectorobtained by calculating the average of all vectors in the cluster G_(i).For example, for G₅={(0,1,0,0) (1,0,0,0)}, the center vector c_(i) is(0.5,0.5,0,0).

Function “D” represents the distance of two vectors. Generally speaking,given two vectors v1=(x₁, x₂, . . . x_(k)) and v2=(y₁, y₂, . . . y_(k)),the distance D(v1, v2) between the vectors v1 and v2 is:D(v1,v2)=∥(x ₁ , . . . , x _(k))−(y ₁ , . . . , y _(k))∥=((x ₁ −y ₁)²+,. . . +(x _(k) −y _(k))²)^(1/2)  Formula (3)

From FIG. 5D and Table 3, a Table 4 may be derived as below.

TABLE 4 Intermediate Target Vector Center Node Paragraph Cluster Space GVector c_(i) D(c_(i), x) ΣD(c_(i), x)² CT₁ F₁ P1 G₁ (1, 0, 0, 0) (1, 0,0, 0) 0 0 F₂ P2 G₂ (0, 0, 1, 0) (0, 0, 1, 0) 0 0 F₃ P3 G₃ (1, 0, 0, 0)(1, 0, 0, 0) 0 0 F₄ P4 G₄ (0, 1, 0, 0) (0, 1, 0, 0) 0 0 P5 (0, 1, 0, 0)0 F₅ P6 G₅ (0, 1, 0, 0) (0.5, 0.5, 0, 0) 0.5^(½) 1 P7 (1, 0, 0, 0)0.5^(½)

For example, as shown in the second row of Table 4, the intermediatenode F₁ has only one child node P1. Accordingly, the cluster G₁ includesonly one vector (1,0,0,0) and its center vector c₁ is (1,0,0,0). Thedistance D(c1,x) of the vector (1,0,0,0) with the center vector c₁ ofthe cluster G₁ is:D(c ₁ , x)=D((1,0,0,0), (1,0,0,0))=0.

As another example, as shown in the fifth row of Table 4, theintermediate node F₅ has two child nodes P6 and P7. Accordingly, thecluster G₅ includes two vectors (0,1,0,0) and (1,0,0,0), and its centervector c₅ is (0.5,0.5,0,0). The distance of the first vector (0,1,0,0)with the center vector c₅ of the cluster G₅ isD((0.5,0.5,0,0),(0,1,0,0))=0.5^(1/2), and The distance of the secondvector (1,0,0,0) with the center vector c₅ isD((0.5,0.5,0,0)(1,0,0,0))=0.5^(1/2).SSE(CT₁)=Σ_(x∈G1) D(c ₁ ,x)²+Σ_(x∈G2) D(c ₂ ,x)²+Σ_(x∈G3) D(c ₃,x)²+Σ_(x∈G4) D(c ₄ ,x)²+Σ_(x∈G5) D(c ₅,x)²=∥(1,0,0,0)−(1,0,0,0)∥²+∥(0,0,1,0)−(0,0,1,0)∥²+∥(1,0,0,0)−(1,0,0,0)∥²+∥(0,1,0,0)−(0,1,0,0)∥²+∥(0,1,0,0)−(0,1,0,0)∥²+(∥(0.5,0.5,0,0)−(0,1,0,0)∥²+∥(0.5,0.5,0,0)−(1,0,0,0)∥²)=0+0+0+0+1=1SSE(CT₁)=Σ_(x∈G1) D(c ₁ ,x)²+Σ_(x∈G2) D(c ₂ ,x)² +Σx _(∈G3) D(c ₃,x)²+Σ_(x∈G4) D(c ₄ ,x)²+Σ_(x∈G5) D(c ₅,x)²=∥(1,0,0,0)−(1,0,0,0)∥²+∥(0,0,1,0)−(0,0,1,0)∥²+∥(1,0,0,0)−(1,0,0,0)∥²+∥(0,1,0,0)−(0,1,0,0)∥²+∥(0,1,0,0)−(0,1,0,0)∥²+(∥(0.5,0.5,0,0)−(0,1,0,0)∥²+∥(0.5,0.5,0,0)−(1,0,0,0)∥²)=0+0+0+0+1=1.

As for the CT3, G1={(1,0,0,0)}, G2={(0,0,1,0)}, G3={(1,0,0,0)}, G4={(0,1,0,0), (0,1,0,0), 0,1,0,0)} and G5={(1,0,0,0)}. Similarly, it can becalculated thatSSE(CT₃)=Σ_(x∈G1)D(c1,x)²+Σ_(x∈G2)D(c2,x)²+Σ_(x∈G3)D(c₃,x)²+Σ_(x∈G4)D(c₄,x)²+Σ_(x∈G5)D(c₅,x)²=0,as shown in the following Table 5.

TABLE 5 Intermediate Target Vector Center Node Paragraph Cluster Space GVector c_(i) D(c_(i), x) ΣD(c_(i), x)² CT₃ F₁ P1 G₁ (1, 0, 0, 0) (1, 0,0, 0) 0 0 F₂ P2 G₂ (0, 0, 1, 0) (0, 0, 1, 0) 0 0 F₃ P3 G₃ (1, 0, 0, 0)(1, 0, 0, 0) 0 0 F₄ P4 G₄ (0, 1, 0, 0) (0, 1, 0, 0) 0 0 P5 (0, 1, 0, 0)0 P6 (0, 1, 0, 0) 0 F₅ P7 G₅ (1, 0, 0, 0) (1, 0, 0, 0) 0 0

Similarly, it may be calculated that SSE(CT₅)=0+0+1+0+0=1, as shown inthe following Table 6.

TABLE 6 Intermediate Target Vector Center Node Paragraph Cluster Space GVector c_(i) D(c_(i), x) ΣD(c_(i), x)² CT₅ F₁ P1 G₁ (1, 0, 0, 0) (1, 0,0, 0) 0 0 F₂ P2 G₂ (0, 0, 1, 0) (0, 0, 1, 0) 0 0 F₃ P3 G₃ (1, 0, 0, 0)(0.5, 0.5, 0, 0) 0.5^(½) 1 P4 G₄ (0, 1, 0, 0) 0.5^(½) F₄ P5 (0, 1, 0, 0)(0, 1, 0, 0) 0 0 P6 (0, 1, 0, 0) 0 F₅ P7 G₅ (1, 0, 0, 0) (1, 0, 0, 0) 00

Similarly, it may be calculated that SSE(CT8)=0+1+0+0+0=1, as shown inthe following Table 7.

TABLE 7 Intermediate Target Vector Center Node Paragraph Cluster Space GVector c_(i) D(c_(i), x) ΣD(c_(i), x)² CT₈ F₁ P1 G₁ (1, 0, 0, 0) (1, 0,0, 0) 0 0 F₂ P2 G₂ (0, 0, 1, 0) (0.5, 0.5, 0, 0) 0.5^(½) 1 P3 (1, 0, 0,0) 0.5^(½) F₃ P4 G₃ (0, 1, 0, 0) (0, 1, 0, 0) F₄ P5 G₄ (0, 1, 0, 0) (0,1, 0, 0) 0 0 P6 (0, 1, 0, 0) 0 F₅ P7 G₅ (1, 0, 0, 0) (1, 0, 0, 0) 0 0

Similarly, it may be calculated that SSE (CT₁₂)=0+1+0+0+0=1, as shown inthe following Table 8.

TABLE 8 Intermediate Target Vector Center Node Paragraph Cluster Space GVector c_(i) D(c_(i), x) ΣD(c_(i), x)² CT₁₂ F₁ P1 G₁ (1, 0, 0, 0) (1, 0,0, 0) 0 0 P2 (0, 0, 1, 0) F₂ P3 G₂ (1, 0, 0, 0) (0.5, 0.5, 0, 0) 0.5^(½)1 0.5^(½) F₃ P4 G₃ (0, 1, 0, 0) (0, 1, 0, 0) F₄ P5 G₄ (0, 1, 0, 0) (0,1, 0, 0) 0 0 P6 (0, 1, 0, 0) 0 F₅ P7 G₅ (1, 0, 0, 0) (1, 0, 0, 0) 0 0

In Step S343, the preferred tree for which the corresponding SSE issmallest is taken as the matching tree.

In the example described above, SSE(CT₃)=0. Therefore, the preferredtree CT₃ is taken as the matching tree. FIG. 4C shows a matching treeMT, which is actually a complete representation of the preferred treeCT₃ as shown in FIG. 4D. In the matching tree MT, nodes P1, P2, P3, P7are child nodes of format nodes F₁, F₂, F₃, and F₅ respectively, andnodes P4, P5 and P5 are child nodes of the format node F₄. Thus it canbe determined that there is a corresponding relation between the sampleparagraph format pattern represented by the format node F₁ and thetarget paragraph represented by the node P1, there is a correspondingrelation between the sample paragraph format pattern represented by theformat node F₂ and the target paragraph represented by the node P2,there is a corresponding relation between the sample paragraph formatpattern represented by the format node F₃ and the target paragraphrepresented by the node P3, there is a corresponding relation betweenthe sample paragraph format pattern represented by the format node F₄and the target paragraphs represented by the node P4, P5 and P6, andthere is a corresponding relation between the sample paragraph formatpattern represented by the format node F₅ and the target paragraphrepresented by the node P7.

After the corresponding relationship between the sample paragraph formatpatterns and the target paragraphs is determined, Step 340 may beperformed in which respective sample paragraph format patternsrepresented by F₁ to F₅ are copied to the corresponded target paragraphsaccording to the determined corresponding relation. For example, for thesample object 210 shown in FIG. 2A and the target object 220 shown inFIG. 2B, the expected object 230 shown in FIG. 2C may be obtained.

Embodiments of the method for copying a text format pattern have beendescribed above. Next, with reference to FIG. 5, embodiments of anapparatus for copying a text format pattern will be described.

As shown in FIG. 6, the apparatus 500 for copying a text format patternaccording to an embodiment of the invention includes a sample selectionreceiving device 510, a copy command receiving device 520, a parsingdevice 530 and a format copying device 540.

The sample selection receiving device 510 is configured to receive theselection of a sample object from a user, the sample object includingmultiple sample paragraphs of which at least two sample paragraphs havedifferent format patterns.

The copy command receiving device 520 is configured to receive aninstruction of format-copying from the user, the instruction indicatingre-formatting a target object with the format pattern of the sampleobject, wherein the target object contains multiple target paragraphs.

The parsing device 530 is configured to determine the correspondingrelationship of the format pattern of the sample paragraphs with thetarget paragraphs.

The format copying device 540 is configured to apply the format patternof the sample paragraphs to the target paragraphs based on thecorresponding relationship determined by the parsing device 530.

According to an embodiment of the invention, the parsing device 530 isfurther configured to determine the corresponding relationship of theformat pattern of the sample paragraphs with the target paragraphs bycomparing the relationship between the format pattern of the sampleparagraphs and the position of the target paragraphs.

According to an embodiment of the invention, the parsing device 530 isfurther configured to determine the corresponding relationship of theformat pattern of the sample paragraphs with the target paragraphs byanalyzing the format pattern of the sample paragraphs and the formatpattern of the target paragraphs.

According to an embodiment of the invention, the parsing device 530 isfurther configured to determine the corresponding relationship betweenthe format pattern of the sample paragraphs and the target paragraphs bycomparing the positional relationship between the sample paragraphs andthe target paragraphs.

According to an embodiment of the invention, the parsing device 530 isfurther configured to determine the corresponding relationship of theformat pattern of the sample paragraphs with the target paragraphsaccording to the relationship between the content of the sampleparagraphs and the content of the target paragraphs.

Various embodiments of the apparatus for copying a text format patternare described above. Since the method for copying a text format patternhas been described in foregoing paragraphs, some of the content that isduplicate with the description of the method is omitted from thedescription of the apparatus.

Compared with prior art techniques, various embodiments of the inventionmay be applied in electronic document processing to copy the formatpattern of one document fragment comprising multiple paragraphs withdifferent format patterns to another document fragment comprisingmultiple paragraphs at a time, thus simplifying the operation of theuser in editing electronic document. The technical effect issignificant.

Embodiments of the invention have been described. The above descriptionis only exemplary, rather than exhaustive or limited to the embodimentsdisclosed. Those skilled in the art shall appreciate that variousmodifications and alterations changes thereto may be readily made. Thechoice of terms herein is intended for best explaining the principle,practical application or improvement to the techniques in the market ofthe embodiments, or allowing those skilled in the art to understandvarious embodiments disclosed herein.

The invention claimed is:
 1. A computer system for reformatting aplurality of target paragraphs with a format pattern of a plurality ofsample paragraphs, the computer system comprising one or more computerprocessors, one or more computer-readable storage media, and programinstructions stored on the one or more computer-readable storage mediafor execution by at least one of the one or more computer processors,the program instructions comprising: program instructions to generate asample combination by dividing the plurality of sample paragraphs intosample groups according to their order and format pattern, each samplegroup includes adjacent sample paragraphs of the same format pattern;program instructions to generate a plurality of different candidatecombinations by assigning the plurality of target paragraphs to thesample groups, wherein each of the plurality of candidate combinationscomprises a different combination of target paragraphs and samplegroups, and each of the plurality of target paragraphs is assigned toonly one sample group for each of the plurality of candidatecombinations; program instructions to select two or more preferredcandidate combinations from the plurality of candidate combinationsbased on a degree of similarity between the sample combination and eachof the plurality of candidate combinations; program instructions toselect a single matching combination from the two or more preferredcandidate combinations; and program instructions to apply the formatpattern of the plurality of sample paragraphs to the plurality of targetparagraphs in accordance with the single matching combination.
 2. Thecomputer system of claim 1, wherein the plurality of sample paragraphsare arranged in a sequential order and each sample paragraph comprises aposition in that sequential order, wherein the plurality of targetparagraphs are arranged in a sequential order and each target paragraphcomprises a position in that sequential order, further comprising:program instructions to compare the format pattern of sample paragraphsand target paragraphs with the same position.
 3. The computer system ofclaim 1, wherein the number of sample groups is less than or equal tothe number of sample paragraphs, and the number of sample groups is lessthan or equal to the number of target paragraphs.
 4. The computer systemof claim 1, further comprising: program instructions to compare apositional relationship between the sample paragraphs to a positionalrelationship between the target paragraphs.
 5. The computer system ofclaim 1, further comprising: program instructions to conduct semanticanalysis on the content of the sample paragraphs and the content of thetarget paragraphs to determine a relationship between the content of thesample paragraphs and the content of the target paragraphs.
 6. Thecomputer system of claim 1, wherein the sample object and the targetobject are from different electronic documents.
 7. The computer systemof claim 1, wherein the possible combinations are determined using theequation: n=(N−1)/[(M−1)! (NM)!], where n is the number of possiblecombinations.
 8. The computer system of claim 1, the content of thesample paragraph is textual and each of the sample paragraphs isseparated by a pre-defined delimiters.
 9. A computer system forreformatting a set of target paragraphs with a format pattern of a setof sample paragraphs, the computer system comprising one or morecomputer processors, one or more computer-readable storage media, andprogram instructions stored on the one or more computer-readable storagemedia for execution by at least one of the one or more computerprocessors, the program instructions comprising: program instructions togenerate a sample combination by dividing the set of sample paragraphsinto sample groups according to their order and format pattern, eachsample group includes adjacent sample paragraphs of the same formatpattern; program instructions to determine a plurality of all possiblecombinations of the set of target paragraphs and the sample groups,wherein each of the target paragraphs is assigned to only one samplegroup for each possible combination; program instructions to calculate adegree of similarity between the sample combination and each of thepossible combinations, the degree of similarity is the sum of the numberof operations of removing a target paragraph from a sample group foreach of the possible combinations and the number of operations of addinga target paragraph to a sample group for each of the possiblecombinations in order for each of the possible combination to match thesample combination; program instructions to select two or more preferredcombinations from the plurality of possible combinations based on thedegree of similarity between the sample combination and each of thepossible combinations, wherein the two or more preferred combinationscomprises the smallest degree of similarity; program instructions toselect a matching combination from the two or more preferred candidatecombinations, the single matching combination having the samecombination of target paragraphs and sample groups as the samplecombination; and program instructions to apply the format pattern of theset of sample paragraphs to the set of target paragraphs based on thesingle matching combination.
 10. The computer system of claim 9, whereinthe plurality of sample paragraphs are arranged in a sequential orderand each sample paragraph comprises a position in that sequential order,wherein the plurality of target paragraphs are arranged in a sequentialorder and each target paragraph comprises a position in that sequentialorder, further comprising: program instructions to compare the formatpattern of sample paragraphs and target paragraphs with the sameposition.
 11. The computer system of claim 9, wherein the number ofsample groups is less than or equal to the number of sample paragraphs,and the number of sample groups is less than or equal to the number oftarget paragraphs.
 12. The computer system of claim 9, furthercomprising: program instructions to compare a positional relationshipbetween the sample paragraphs to a positional relationship between thetarget paragraphs.
 13. The computer system of claim 9, furthercomprising: program instructions to conduct semantic analysis on thecontent of the sample paragraphs and the content of the targetparagraphs to determine a relationship between the content of the sampleparagraphs and the content of the target paragraphs.
 14. The computersystem of claim 9, wherein the sample object and the target object arefrom different electronic documents.
 15. The computer system of claim 9,wherein the possible combinations are determined using the equation:n=(N−1)/[(M−1)! (NM)!], where n is the number of possible combinations.16. The computer system of claim 9, the content of the sample paragraphis textual and each of the sample paragraphs is separated by apre-defined delimiters.